• Class and Course

    Applied Geostatistics

    Business context: An appreciation of what geostatistics can achieve is now essential in nearly all important aspects of exploration and production: gridding and contouring for making maps, upscaling for reservoir simulation and basin modelling, as well as the analysis of spatially referenced data of all kinds. Without needing to know the details of the algorithms and the mathematics behind them, being able to choose the most appropriate techniques and apply them correctly is fundamental to best practice throughout E&P. Who should attend: Petroleum geologists and other geoscientists preparing data for use in reservoir simulators; engineers involved with exploration and development of oil and gas reservoirs; anyone wishing to gain the best insight into and obtain the most value from their geo-spatial data.

    The course aims to provide knowledge of how to apply the various tools known as geostatistics, using both readily available software and more specialist packages. Learning, methods and tools. The emphasis is on practical application and understanding of context over a consideration of the mathematics. The course includes using software for worked exercises, which give a practical introduction to what is available as well as providing useful tools to take back to the workplace.

    Day 1

    Establish why stochastic methods are useful in reservoir modelling. Use this to plan which statistical ideas and methods we need to study.

    Goals - At the end of the module you should:

    • appreciate why this course is important
    • be able place the remainder of the course in a context of what is useful in geology.

    The data

    • preparation
    • exploratory analysis

    Statistical basics

    • use of basic tools for summarising and understanding data
    • use of basic tools for preparing data for further statistical analysis or for geological analysis

    Goals - At the end of the module you should:

    • be able to choose suitable techniques for analysis of data
    • use every day tools such as Microsoft Excel® to check data quality and undertake the basic statistical analysis
    • prepare data, using such common tools, for input to Petrel
    Day 2

    Why do we need statistics?

    The data requirements of a simulator

    • collecting data together from different disciplines, on different support, for modelling.

    Goals - At the end of the module you should:

    • understand what to do when inferring from incomplete data: e.g. known values at wells, what happens between?
    • know what to do when we cannot measure a parameter directly but have to predict from one we can measure

    Sources of error

    • confidence
    • variance
    • reliability

    Goals - At the end of the module you should:

    • be able to quantify the confidence in a prediction.
    • know limitations of Petrel, when to transform and when to re-analyse.
    Day 3

    How does a spatial dependence in the data affect statistics?

    How to analyse data for spatial dependence.

    Goals - At the end of these modules you should:

    • be able to choose suitable techniques for analysis of data
    • use everyday tools such as Microsoft Excel® to undertake the basic statistical analysis
    • Take a spatial data set and plot it using Excel®

    Geostatistical basics

    • use of basic tools

    Goals - At the end of the module you should:

    • be able to establish trends
    • use tools such as directional semivariograms as tools in their own right
    Day 4

    Support and upscaling

    • averaging and simulation techniques
    • static and dynamic data

    Goals - At the end of this module you should:

    • be able to correctly choose techniques for upscaling different data types
    • be able to distinguish correctly upscaled data from badly upscaled data

    Estimation, including Kriging:

    • understand the basics
    • simple exercises to illustrate how kriging incorporates spatial variation and allows estimation of variance
    • Using co-located co-kriging for better prediction of important parameters betwee
    Day 5

    More advanced techniques

    • Making use of new data:
      • History matching
      • Bayesian statistics
    • Objects:
      • Sequential / Indicator simulation
    • Object modelling in Petrel:
      • defining objects
      • incorporating objects in models
    • Multi-point statistics:
      • limitations of semivariograms
      • combining with object modelling

    Goals - At the end of this module you should:

    • be able to correctly and appropriately use techniques presented in Petrel.

    Recap and reinforcement:

    • cover any techniques shown by the exercises to be shaky

    Goals - At the end of this module you should:

    • leave the course with a firm understanding of the basics and the ability to use all of the techniques covered in the course, in everyday work
    • know how to re-familiarise (get back up to speed) after not using the techniques for any length of time

    Geoscientists and Engineers

    What is geostatistics and how does it change our appreciation of familiar tasks and tools?

    How geostatistics aids in understanding trends in spatial data-sets:

    o Classical multivariate statistics

    o Conditional distributions

    o Direct simulations

    o Variogram analysis

    o Modelling anisotropy

    Understanding the effects of scale:

    o Heterogeneity and discontinuity

    o Data scale versus modelling scale

    o Upscaling for efficient modelling

    Allowing for spatial trends in gridding & contouring:

    o Honouring data or minimising errors

    o Using kriging to make better maps

    Making use of new data:

    o Bayesian and geo- statistics

    o History matching

    Sequential / Indicator simulation

    Quantifying uncertainty:

    o How geostatistics includes methods for uncertainty quantification

    o Using Monte Carlo and other stochastic simulations


    Basic Knowledge of Subsurface Characterization and Excel®

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